# Are All Steps Equally Important? Benchmarking Essentiality Detection of Events

arXiv: https://arxiv.org/abs/2210.04074 · HTML: https://mlsanity.com/paper/2210.04074/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [UnifiedQA: Crossing Format Boundaries With a Single QA System](https://mlsanity.com/paper/2005.00700/) (2020)
- [WikiHow: A Large Scale Text Summarization Dataset](https://mlsanity.com/paper/1810.09305/) (2018)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [A Multi-Axis Annotation Scheme for Event Temporal Relations](https://mlsanity.com/paper/1804.07828/) (2018)
- [Joint Reasoning for Temporal and Causal Relations](https://mlsanity.com/paper/1906.04941/) (2019)
- [Joint Constrained Learning for Event-Event Relation Extraction](https://mlsanity.com/paper/2010.06727/) (2020)
- [Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2009.13656/) (2020)
- [ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities](https://mlsanity.com/paper/2104.02137/) (2021)
- [Unsupervised Label-aware Event Trigger and Argument Classification](https://mlsanity.com/paper/2012.15243/) (2020)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)
- [Intent Detection with WikiHow](https://mlsanity.com/paper/2009.05781/) (2020)
- ["What Are You Trying to Do?" Semantic Typing of Event Processes](https://mlsanity.com/paper/2010.06724/) (2020)
- [ROCK: Causal Inference Principles for Reasoning about Commonsense Causality](https://mlsanity.com/paper/2202.00436/) (2022)
- [Learning Constraints and Descriptive Segmentation for Subevent Detection](https://mlsanity.com/paper/2109.06316/) (2021)

## Built on by (most cited first, 1 of 1)

- [Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought](https://mlsanity.com/paper/2410.05695/) (2024)

## Abstract (arXiv)

Natural language expresses events with varying granularities, where coarse-grained events (goals) can be broken down into finer-grained event sequences (steps). A critical yet overlooked aspect of understanding event processes is recognizing that not all step events hold equal importance toward the completion of a goal. In this paper, we address this gap by examining the extent to which current models comprehend the essentiality of step events in relation to a goal event. Cognitive studies suggest that such capability enables machines to emulate human commonsense reasoning about preconditions and necessary efforts of everyday tasks. We contribute a high-quality corpus of (goal, step) pairs gathered from the community guideline website WikiHow, with steps manually annotated for their essentiality concerning the goal by experts. The high inter-annotator agreement demonstrates that humans possess a consistent understanding of event essentiality. However, after evaluating multiple statistical and largescale pre-trained language models, we find that existing approaches considerably underperform compared to humans. This observation highlights the need for further exploration into this critical and challenging task. The dataset and code are available at http://cogcomp.org/page/publication_view/1023.